Recent applications of machine learning in alloy design: A review

M Hu, Q Tan, R Knibbe, M Xu, B Jiang, S Wang… - Materials Science and …, 2023 - Elsevier
The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

A review of grain boundary and heterointerface characterization in polycrystalline oxides by (scanning) transmission electron microscopy

H Vahidi, K Syed, H Guo, X Wang, JL Wardini… - Crystals, 2021 - mdpi.com
Interfaces such as grain boundaries (GBs) and heterointerfaces (HIs) are known to play a
crucial role in structure-property relationships of polycrystalline materials. While several …

Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character

ER Homer, GLW Hart, CB Owens, DM Hensley… - Acta Materialia, 2022 - Elsevier
The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic
degrees of freedom that define the character of a grain boundary. While numerous datasets …

Universal function for grain boundary energies in bcc metals

O Chirayutthanasak, R Sarochawikasit, S Khongpia… - Scripta Materialia, 2024 - Elsevier
Constructing microstructure-property-processing relationships in polycrystalline metals
remains a challenge mainly due to the lack of quantitative relations between grain boundary …

A universal machine learning model for elemental grain boundary energies

W Ye, H Zheng, C Chen, SP Ong - Scripta Materialia, 2022 - Elsevier
The grain boundary (GB) energy has a profound influence on the grain growth and
properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by …

Prediction of vacancy formation energies at tungsten grain boundaries from local structure via machine learning method

Y Wang, X Li, X Li, Y Zhang, Y Zhang, Y Xu… - Journal of Nuclear …, 2022 - Elsevier
Grain boundary (GB) plays a crucial role in the mechanical properties and irradiation
resistance of nuclear materials. It is thus essential to understand and predict the defect …

A roadmap from the bond strength to the grain-boundary energies and macro strength of metals

X Li, H Wu, W Gao, Q Jiang - Nature Communications, 2025 - nature.com
Correlating the bond strength with the macro strength of metals is crucial for understanding
mechanical properties and designing multi-principal-element alloys (MPEAs). Motivated by …

Five degree-of-freedom property interpolation of arbitrary grain boundaries via Voronoi fundamental zone framework

SG Baird, ER Homer, DT Fullwood… - Computational Materials …, 2021 - Elsevier
We introduce the Voronoi fundamental zone (VFZ) framework which is useful for grain
boundary (GB) structure–property models and gaining insights about the nature of a five …

Design of medium carbon steels by computational intelligence techniques

NS Reddy, J Krishnaiah, HB Young, JS Lee - Computational Materials …, 2015 - Elsevier
Steel design with the targeted properties is a challenging task due to the involvement of
many variables and their complex interactions. Artificial neural networks (ANN) recognized …

Prediction of fatigue stress concentration factor using extreme learning machine

B Wang, W Zhao, Y Du, G Zhang, Y Yang - Computational materials …, 2016 - Elsevier
Fatigue stress concentration factor (FSCF) plays a vital role in studying the limitation of
material fatigue resistance. Theoretically, FSCF not only reflects the level of fatigue stress …